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A multi-phase deep CNN based mitosis detection framework for breast cancer histopathological images

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Sohail, Anabia, Khan, Asifullah, Wahab, Noorul, Zameer, Aneela and Khan, Saranjam (2021) A multi-phase deep CNN based mitosis detection framework for breast cancer histopathological images. Scientific Reports, 11 (1). 6215. doi:10.1038/s41598-021-85652-1

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Official URL: https://doi.org/10.1038/s41598-021-85652-1

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Abstract

The mitotic activity index is a key prognostic measure in tumour grading. Microscopy based detection of mitotic nuclei is a significant overhead and necessitates automation. This work proposes deep CNN based multi-phase mitosis detection framework “MP-MitDet” for mitotic nuclei identification in breast cancer histopathological images. The workflow constitutes: (1) label-refiner, (2) tissue-level mitotic region selection, (3) blob analysis, and (4) cell-level refinement. We developed an automatic label-refiner to represent weak labels with semi-sematic information for training of deep CNNs. A deep instance-based detection and segmentation model is used to explore probable mitotic regions on tissue patches. More probable regions are screened based on blob area and then analysed at cell-level by developing a custom CNN classifier “MitosRes-CNN” to filter false mitoses. The performance of the proposed “MitosRes-CNN” is compared with the state-of-the-art CNNs that are adapted to cell-level discrimination through cross-domain transfer learning and by adding task-specific layers. The performance of the proposed framework shows good discrimination ability in terms of F-score (0.75), recall (0.76), precision (0.71) and area under the precision-recall curve (0.78) on challenging TUPAC16 dataset. Promising results suggest good generalization of the proposed framework that can learn characteristic features from heterogenous mitotic nuclei.

Item Type: Journal Article
Divisions: Faculty of Science > Computer Science
SWORD Depositor: Library Publications Router
Journal or Publication Title: Scientific Reports
Publisher: Nature Publishing Group UK
ISSN: 2045-2322
Official Date: 18 March 2021
Dates:
DateEvent
18 March 2021Published
16 February 2021Accepted
Volume: 11
Number: 1
Article Number: 6215
DOI: 10.1038/s41598-021-85652-1
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access

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